TopoPilot is an agentic framework designed to automate complex scientific visualization workflows, particularly in topological data analysis and visualization. It uses a two‑agent architecture—a user‑prompt orchestrator and a verifier—to translate natural language into atomic backend actions and ensure semantic consistency before execution. The system incorporates deterministic guardrails and a taxonomy of failure modes, achieving over 95% success in 1,000 simulated multi‑turn conversations and outperforming baseline models and other code generation tools in accuracy, completion time, and token usage.
arXiv:2604. 27996v3 Announce Type: replace Abstract: This paper examines how large language model (LLM) agents perform on scientific visualization (SciVis) tasks that require generating visualization workflows from natural-language instructions.
arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).
RSIV-Harness is a framework that keeps language model weights fixed while recursively improving the surrounding harness based on failure traces, using a four-layer evaluation to ensure stability. It addresses challenges in open-domain conversational text-to-visualization, such as ambiguous user requests, data source discovery, and iterative chart refinement. The introduced ODAC-Vis benchmark evaluates these capabilities across 640 tasks, and experiments show significant gains in executability and chart structure after a single RSIV-Harness iteration.
arXiv:2606. 05525v1 Announce Type: new Abstract: Recent advances in agentic visualization have enabled the translation of natural language into executable scientific visualization (SciVis) workflows.
arXiv:2608. 16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language.
ChatDev 2.0, also called DevAll, is a no-code platform that lets users build, run, and inspect heterogeneous multi‑agent systems (MAS) powered by large language models. It combines a declarative executable graph abstraction with a cycle‑aware execution engine, enabling representation and execution of dynamic, cyclic interactions among diverse agents. The integrated visual interface allows users to author, monitor, and inspect MAS—including human‑in‑the‑loop steps—without writing code, and experiments show it matches state‑of‑the‑art MAS performance across three tasks.
arXiv:2606. 18425v1 Announce Type: cross Abstract: Scientific workflow management systems (WMS) support scalable and reproducible execution of complex pipelines, but workflow design, implementation, and debugging remain largely manual and require significant expertise.
arXiv:2603. 29139v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled agentic systems to translate natural-language intent into executable scientific visualization (SciVis) tasks.
arXiv:2607. 16617v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate data-processing workflows, yet coding agents typically produce scripts that are not automatically materialized as persistent, editable platform artifacts.
Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language. Although these s...
The paper introduces KNOWS, a benchmark for evaluating web agents that act as assistants by retrieving, synthesizing, and presenting information across complex, multi-step browser tasks. It outlines a task design rubric, evaluation protocol combining deterministic checks with LLM judgments, and reports that current agents achieve only modest success, with the best performing agent succeeding on less than 3% of tasks. The study highlights significant gaps in agents’ tool use, visual understanding, and long‑horizon reasoning.
arXiv:2609.24094v1 Announce Type: cross Abstract: Visual analytics (VA) enables sensemaking through interactive visualization, but effective analysis often requires experts to translate high-level in...